Case matching, also known as similarity matching, has been used to provide inferences about solving a problem using evidence from past examples or cases. Case matching can be used to assist health-care providers by making a treatment recommendation using information from similar past patient cases. Historical patient data can assist care providers with predicting the outcomes of particular clinical interventions on a patient. Finding similar patients in a database is a time and resource intensive process. There is a trade-off between having a large enough database that increases the chances of finding similar patients and the amount of time taken to search a large database. The similarity matching algorithm, that finds patients similar to the currently treated patient, must respond quickly to be effective and not impose its own time requirements on the patient treatment process. In addition, the cases that are found must be evaluated with regard to their relevance to the current case. Medical classification systems exist, such as ICD-9 (International Classification of Diseases, version 9). ICD-9 codes are diagnosis codes that classify diseases, signs, symptoms, abnormal findings, complaints, social circumstances and external causes of injury or disease (ICD-9-CM, refers to the Clinical Modification extension designed to capture more morbidity data and the addition of procedure codes). These classification codes are assigned to the patient after being discharged from the clinical setting. Typically, these codes are used for morbidity and mortality statistics, and in the USA for reimbursement systems.
A problem with case matching is that it is very difficult of performing a matching between patients who have overlapping medical conditions.